[Feature] Unify the registration name recognition for tool_parser and reasoning_parser to “-” (#4668)

* parser register name unify

* change ernie_x1 to ernie-x1

* change ernie4_5_vl to ernie-45-vl

* fix unit test
This commit is contained in:
kxz2002
2025-10-31 10:45:27 +08:00
committed by GitHub
parent 82bd7e5db4
commit a2870ed4a9
16 changed files with 28 additions and 26 deletions

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@@ -1,5 +1,5 @@
reasoning-parser: ernie_x1
tool_call_parser: ernie_x1
reasoning-parser: ernie-x1
tool_call_parser: ernie-x1
tensor_parallel_size: 4
max_model_len: 65536
max_num_seqs: 128

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@@ -1,7 +1,7 @@
tensor_parallel_size: 1
max_model_len: 131072
max_num_seqs: 32
reasoning_parser: ernie_x1
tool_call_parser: ernie_x1
reasoning_parser: ernie-x1
tool_call_parser: ernie-x1
load_choices: "default_v1"
quantization: wint8

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@@ -33,8 +33,8 @@ python -m fastdeploy.entrypoints.openai.api_server \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--quantization wint8 \
--reasoning-parser ernie_x1 \
--tool-call-parser ernie_x1 \
--reasoning-parser ernie-x1 \
--tool-call-parser ernie-x1 \
--max-num-seqs 32
```
- `--quantization`: Indicates the quantization strategy used by the model. Different quantization strategies will result in different performance and accuracy of the model. It could be one of `wint8` / `wint4` / `block_wise_fp8`(Hopper is needed).

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@@ -80,7 +80,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
# Whether to use Machete for wint4 dense GEMM.
"FD_USE_MACHETE": lambda: os.getenv("FD_USE_MACHETE", "1"),
# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie4_5_vl, \n</think>\n\n for ernie_x1)
# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie-45-vl, \n</think>\n\n for ernie-x1)
"FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR": lambda: os.getenv("FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR", "</think>"),
# Timeout for cache_transfer_manager process exit

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@@ -33,8 +33,8 @@ python -m fastdeploy.entrypoints.openai.api_server \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--quantization wint8 \
--reasoning-parser ernie_x1 \
--tool-call-parser ernie_x1 \
--reasoning-parser ernie-x1 \
--tool-call-parser ernie-x1 \
--max-num-seqs 32
```
其中:

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@@ -80,7 +80,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
# 是否使用 Machete 后端的 wint4 GEMM.
"FD_USE_MACHETE": lambda: os.getenv("FD_USE_MACHETE", "1"),
# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie4_5_vl, \n</think>\n\n for ernie_x1)
# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie-45-vl, \n</think>\n\n for ernie-x1)
"FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR": lambda: os.getenv("FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR", "</think>"),
# cache_transfer_manager 进程残留时退出等待超时时间

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@@ -95,6 +95,7 @@ class ToolParserManager:
Raise a KeyError exception if the name is not registered.
"""
name = name.replace("_", "-")
if name in cls.tool_parsers:
return cls.tool_parsers[name]

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@@ -44,7 +44,7 @@ from fastdeploy.entrypoints.openai.tool_parsers.abstract_tool_parser import (
from fastdeploy.utils import data_processor_logger
@ToolParserManager.register_module("ernie_45-vl-thinking")
@ToolParserManager.register_module("ernie-45-vl-thinking")
class Ernie45VLThinkingToolParser(ToolParser):
"""
Tool parser for Ernie model version 4.5.1.

View File

@@ -44,7 +44,7 @@ from fastdeploy.entrypoints.openai.tool_parsers.abstract_tool_parser import (
from fastdeploy.utils import data_processor_logger
@ToolParserManager.register_module("ernie_x1")
@ToolParserManager.register_module("ernie-x1")
class ErnieX1ToolParser(ToolParser):
"""
Tool parser for Ernie model version 4.5.1.

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@@ -122,7 +122,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
"FD_ENABLE_SWAP_SPACE_CLEARING": lambda: int(os.getenv("FD_ENABLE_SWAP_SPACE_CLEARING", "0")),
# enable return text, used when FD_ENABLE_INTERNAL_ADAPTER=1
"FD_ENABLE_RETURN_TEXT": lambda: bool(int(os.getenv("FD_ENABLE_RETURN_TEXT", "0"))),
# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie4_5_vl, \n</think>\n\n for ernie_x1)
# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie-45-vl, \n</think>\n\n for ernie-x1)
"FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR": lambda: os.getenv("FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR", "</think>"),
# Timeout for cache_transfer_manager process exit
"FD_CACHE_PROC_EXIT_TIMEOUT": lambda: int(os.getenv("FD_CACHE_PROC_EXIT_TIMEOUT", "600")),

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@@ -101,7 +101,7 @@ def limit_thinking_content_length(
line_break_id: int = None,
):
if limit_strategy == "</think>":
# for ernie4_5_vl
# for ernie-45-vl
limit_thinking_content_length_v1(
sampled_token_ids,
max_think_lens,
@@ -110,7 +110,7 @@ def limit_thinking_content_length(
think_end_id,
)
elif limit_strategy == "\n</think>\n\n":
# for ernie_x1
# for ernie-x1
assert line_break_id > 0
limit_thinking_content_length_v2(
sampled_token_ids,
@@ -136,7 +136,7 @@ def speculate_limit_thinking_content_length(
line_break_id: int = None,
):
if limit_strategy == "</think>":
# for ernie4_5_vl
# for ernie-45-vl
speculate_limit_thinking_content_length_v1(
accept_tokens,
max_think_lens,
@@ -147,7 +147,7 @@ def speculate_limit_thinking_content_length(
think_end_id,
)
elif limit_strategy == "\n</think>\n\n":
# for ernie_x1
# for ernie-x1
assert line_break_id > 0
speculate_limit_thinking_content_length_v2(
accept_tokens,

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@@ -125,6 +125,7 @@ class ReasoningParserManager:
Raise a KeyError exception if the name is not registered.
"""
name = name.replace("_", "-")
if name in cls.reasoning_parsers:
return cls.reasoning_parsers[name]

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@@ -5,10 +5,10 @@ from fastdeploy.entrypoints.openai.protocol import ChatCompletionRequest, DeltaM
from fastdeploy.reasoning import ReasoningParser, ReasoningParserManager
@ReasoningParserManager.register_module("ernie_x1")
@ReasoningParserManager.register_module("ernie-x1")
class ErnieX1ReasoningParser(ReasoningParser):
"""
Reasoning parser for ernie_x1 model with stricter boundary checking.
Reasoning parser for ernie-x1 model with stricter boundary checking.
Unified rules:
- Do not strip newline before </think>

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@@ -203,7 +203,7 @@ def xpu_post_process(
step_idx = share_inputs["step_idx"]
limit_think_status = share_inputs["limit_think_status"]
if limit_strategy == "</think>":
# for ernie4_5_vl
# for ernie-45-vl
limit_thinking_content_length_v1(
sampled_token_ids,
max_think_lens,
@@ -212,7 +212,7 @@ def xpu_post_process(
think_end_id,
)
elif limit_strategy == "\n</think>\n\n":
# for ernie_x1
# for ernie-x1
assert line_break_id > 0
limit_thinking_content_length_v2(
sampled_token_ids,

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@@ -73,9 +73,9 @@ class TestOpenAIServingCompletion(unittest.TestCase):
self.assertTrue(serving_completion._check_master())
def test_calc_finish_reason_tool_calls(self):
# 创建一个模拟的engine_client并设置reasoning_parser为"ernie_x1"
# 创建一个模拟的engine_client并设置reasoning_parser为"ernie-x1"
engine_client = Mock()
engine_client.reasoning_parser = "ernie_x1"
engine_client.reasoning_parser = "ernie-x1"
# 创建一个OpenAIServingCompletion实例
serving_completion = OpenAIServingCompletion(engine_client, None, "pid", "ips", 360)
# 创建一个模拟的output并设置finish_reason为"tool_call"
@@ -86,9 +86,9 @@ class TestOpenAIServingCompletion(unittest.TestCase):
assert result == "tool_calls"
def test_calc_finish_reason_stop(self):
# 创建一个模拟的engine_client并设置reasoning_parser为"ernie_x1"
# 创建一个模拟的engine_client并设置reasoning_parser为"ernie-x1"
engine_client = Mock()
engine_client.reasoning_parser = "ernie_x1"
engine_client.reasoning_parser = "ernie-x1"
# 创建一个OpenAIServingCompletion实例
serving_completion = OpenAIServingCompletion(engine_client, None, "pid", "ips", 360)
# 创建一个模拟的output并设置finish_reason为其他值

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@@ -91,7 +91,7 @@ class TestReasoningParserManager(unittest.TestCase):
Test that a parser can be registered and retrieved successfully.
Verifies normal registration and retrieval functionality.
"""
ReasoningParserManager.register_module(module=TestReasoningParser, name="test_parser", force=True)
ReasoningParserManager.register_module(module=TestReasoningParser, name="test-parser", force=True)
parser_cls = ReasoningParserManager.get_reasoning_parser("test_parser")
self.assertIs(parser_cls, TestReasoningParser)